Pathologists routinely use microscopes to assess patterns in tissue samples on glass slides. The assessment of these patterns, which may indicate the presence of malignancies, for example, is often critical to the diagnosis of disease in humans and animals. Because human observers conduct such assessments, they are subjective. Subjectivity in diagnosis can lead to misdiagnosis, which negatively impact patients, care providers, and payers.
Specialized instruments called whole-slide scanners can capture an image of the tissue sample on a glass slide that has resolution and clarity equivalent to that provided by a microscope. Whole slide scanners output whole-slide images that can be viewed on a computer workstation and analyzed by software.
Some machine learning techniques are trained by thousands (or more) of examples of categories for classifying new data. Once trained and given new data for classification, such techniques can classify the new data, e.g., by specifying a constituent class or providing probabilities of membership in each class.